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A Study on P2P Lending Deadline Prediction Model based on Machine Learning
http://doi.org/10.5626/JOK.2019.46.2.174
Recently, there has been an increase in P2P lending users, a product that supports investments through lending among individuals using online platforms. However, since P2P lending`s investors have to take financial risks, the investors may fail to investment due to the close of investment while they considering whether to invest or not. This paper predicts how long an investment product will take from a certain point to the close in order to provide deadline information for P2P loan investment products. To predicts the investment deadline, we have transforms into Timeseries data and Step data based on investment information on actual P2P products. The regression, classification, and time series prediction model were generated using machine learning algorithm. The results of the performance evaluation showed that in the Timeseries data-based model, the Multi-layer Perceptron regression model and the classification model showed the highest performance at 0.725 and 0.703 respectively. The Step data-based model was also the highest with the Multi-layer Perceptron regression model and the classification model at 0.782 and 0.651 respectively.
Diagnostic and Therapeutic Model for Korean Major Depressive Disorder Using Multi-Modal Data
Yonghwa Choi, Aram Kim, Minji Jeon, Sunkyu Kim, Kyu-Man Han, Eunsoo Won, Byung-Joo Ham, Jaewoo Kang
http://doi.org/10.5626/JOK.2019.46.1.71
Depression is one of the most common mental illnesses in the modern society, and it increases the social burden due to repeated recurrences. However, since there are many pre-disposing factors that cause depression, there is need to develop a machine-learning model that examine these factors effectively. In this paper, we propose a model that can diagnose depression and predict the degree of antidepressant response using four multi modal data including basic information, MRI, genetics, and cognitive test. The model achieved 0.923 AUROC score for diagnosis and 0.08 MSE for prediction of antidepressant response. In addition, the results of the proposed model were quantitatively analyzed, and it confirmed that accurate diagnosis and drug response prediction are possible when the patient’s data is added. Qualitative analysis was also conducted to provide new hypotheses as well as findings on the main factors causing depression.
Korean Machine Reading Comprehension with S²-Net
Cheoneum Park, Changki Lee, Sulyn Hong, Yigyu Hwang, Taejoon Yoo, Hyunki Kim
http://doi.org/10.5626/JOK.2018.45.12.1260
Machine reading comprehension is the task of understanding a given context and identifying the right answer in context. Simple recurrent unit (SRU) solves the vanishing gradient problem in recurrent neural network (RNN) by using neural gate such as gated recurrent unit (GRU), and removes previous hidden state from gate input to improve speed. Self-matching network is used in r-net, and this has a similar effect as coreference resolution can show similar semantic context information by calculating attention weight for its RNN sequence. In this paper, we propose a S²-Net model that add self-matching layer to an encoder using stacked SRUs and constructs a Korean machine reading comprehension dataset. Experimental results reveal the proposed S²-Net model has EM 70.81% and F1 82.48% performance in Korean machine reading comprehension.
Semi-Supervised Learning for Detecting of Abusive Sentence on Twitter using Deep Neural Network with Fuzzy Category Representation
http://doi.org/10.5626/JOK.2018.45.11.1185
The number of people embracing damage caused by hate speech on the SNS(Social Network Service) is increasing rapidly. In this paper, we propose a detection method using Semi-supervised learning and Deep Neural Network from a large file to determine whether implied meaning of sentence beyond hate speech detection through comparison with a simple dictionary in twitter sentence is abusive or not. Most of the methods judge the hate speech sentence by comparing with a blacklist comprising of hate speech words. However, the reported methods have a disadvantage that skillful and subtle expression of hate speech cannot be identified. So, we created a corpus with a label on whether or not to hate speech on Korean twitter sentence. The training corpus in twitter comprised of 44,000 sentences and the test corpus comprised of 13,082 sentences. The system performance about the explicit abusive sentences of the F1 score was 86.13% on the model using 1-layer syllable CNN and sequence vector. And the system performance about the implicit abusive sentences of the F1 score 25.53% on the model using 1-layer syllable CNN and 2-layer syllable CNN and sequence vector. The proposed method can be used as a method for detecting cyber-bullying.
A Twitter News-Classification Scheme Using Semantic Enrichment of Word Features
Seonmi Ji, Jihoon Moon, Hyeonwoo Kim, Eenjun Hwang
http://doi.org/10.5626/JOK.2018.45.10.1045
Recently, with the popularity of Twitter as a news platform, many news articles are generated, and various kinds of information and opinions about them spread out very fast. But since an enormous amount of Twitter news is posted simultaneously, users have difficulty in selectively browsing for news related to their interests. So far, many works have been conducted on how to classify Twitter news using machine learning and deep learning. In general, conventional machine learning schemes show data sparsity and semantic gap problems, and deep learning schemes require a large amount of data. To solve these problems, in this paper, we propose a Twitter news-classification scheme using semantic enrichment of word features. Specifically, we first extract the features of Twitter news data using the Vector Space Model. Second, we enhance those features using DBpedia Spotlight. Finally, we construct a topic-classification model based on various machine learning techniques and demonstrate by experiments that our proposed model is more effective than other traditional methods.
An Efficient and Adaptable Hybrid Multi-channel Multi-hop MAC Protocol in VANETs
VanDung Nguyen, Eui-Nam Huh, Choong Seon Hong
http://doi.org/10.5626/JOK.2018.45.10.981
Vehicular Ad-hoc NETworks (VANETs) are designed to improve transportation efficiency such as to increase safety and reduce traffic accidents. In addition, VANET is created to connect and exchange information between vehicles or between vehicle and infrastructure. For VANET, Medium Access Control (MAC) protocols, which provide an efficient broadcast service, are designed to efficiently and fairly share the wireless medium between vehicles and providers. Recently, the hybrid MAC protocol was designed to combine TDMA- and CSMA-based mechanisms into a single mechanism to improve the Quality of Service (QoS) and decrease the collision rate. In this paper, we propose an Efficient and Adaptable Hybrid Multi-channel Multi-hop MAC protocol in VANETs, called the EAHMAC protocol, which allows vehicles to not only occupy time slots but also to broadcast packets in a flexible way based on the two-hop neighbor"s information. The simulation results show that our proposal outperforms the existing protocols in terms of access collision rate, packet delivery ratio, and throughput on the service channel.
Collecting Network Field Information using Machine Learning
Kyu Seok Han, Taekyu Kim, Shinwoo Shim, Sung Goo Jun, Jiwon Yoon
http://doi.org/10.5626/JOK.2018.45.10.1096
Recently, various systems based on Internet of Things (IOT) and Information and Communications Technologies(ICT) have been developed. Today, assorted devices are connected to a network, and various operating systems according to devices having different resources and functions have appeared. With the increased need for in hacking security, researches on the vulnerability analysis of the operating system installed on each device and the actual attack technique have been carried out. Accordingly, the type and detailed version of the operating system of the device, Function (API) is emerging as important information in security. Since the control of this information gathering in the cyber warfare is the first stage of the cyber threat, many studies have been conducted on mehods for controlling the network traffic while scanning. In order to bypass this control of the network, information collectors prepare countermeasures to secretly collect port information. In this paper, we deal with a scanning method that can acquire information about opponents through network basic commands which are not important in the network control system.
Korean Machine Reading Comprehension using Reinforcement Learning and Dual Co-Attention Mechanism
http://doi.org/10.5626/JOK.2018.45.9.932
Machine Reading Comprehension is a question-answering model for the purposes of understanding a given document and then finding the correct answer within the document. Previous studies on the Machine Reading Comprehension model have been based on end-to-end neural network models with various attention mechanisms. However, in the previous models, difficulties arose when attempting to find answers with long dependencies between lexical clues because these models did not use grammatical and syntactic information. To resolve this problem, we propose a Machine Reading Comprehension model with a dual co-attention mechanism reflecting part-of-speech information and shortest dependency path information. In addition, to increase the performances, we propose a reinforce learning method using F1-scores of answer extraction as rewards. In the experiments with 18,863 question-answering pairs, the proposed model showed higher performances (exact match: 0.4566, F1-score: 0.7290) than the representative previous model.
A Linguistic Study of Speech Act and Automatic Speech Act Classification for Korean Tutorial Dialog
Youngeun Koo, Jiyoun Kim, Munpyo Hong, Youngkil Kim
http://doi.org/10.5626/JOK.2018.45.8.807
Speech act is a speaker’s intention of utterance in communication. To communicate successfully, we need to figure out speech act of a speaker’s utterance correctly. This paper proposed linguistic features of an utterance that affect speech act classification by analyzing Korean tutorial dialogue. Ultimately we hope this enables automatic speech act classification. Thirteen linguistically motivated features are suggested in this paper and verified with WEKA 3.8.1. The accuracy of the proposed linguistically motivated features of speech act classification reached 70.03%. Approximately 30%p of accuracy has improved compared to a baseline, using unigram and bigram as the only features of speech act classification.
A Study on Two-dimensional Array-based Technology to Identify Obfuscatied Malware
Seonbin Hwang, Hogyeong Kim, Junho Hwang, Taejin Lee
http://doi.org/10.5626/JOK.2018.45.8.769
More than 1.6 milion types of malware are emerging on average per day, and most cyber attackes are generated by malware. Moreover, malware obfuscation techniques are becoming more intelligent through packing or encryption to prevent reverse engineering analysis. In the case of static analysis, there is a limit to the analysis when the analytical file becomes obfuscated, and a countermeasure is needed. In this paper, we propose an approach based on String, Symbol, and Entropy as a way to identify malware even during obfuscation. Two-dimensional arrays were applied for fixed feature-set processing as well as non-fixed feature-set processing, and 15,000 malware/benign samples were tested using the Deep Neural Network. This study is expected to operate in a complementary manner in conjunction with various malicious code detection methods in the future, and it is expected that it can be utilized in the analysis of obfuscated malware variants.
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